Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis.
Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis.
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DOI：
10.1371/journal.pcbi.1004465
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发表时间：
2015-12
影响因子：
4.3
通讯作者：
Kuang R
中科院分区：
文献类型：
--
作者：
Zhang W;Chang JW;Lin L;Minn K;Wu B;Chien J;Yong J;Zheng H;Kuang R
高通量mRNA测序（RNA-Seq）广泛用于基因亚型的转录本定量。由于单独的RNA-Seq数据通常不足以准确地识别来自异构体的读取起源以进行定量，因此我们建议探索蛋白质结构域-结构域相互作用作为与RNA-Seq数据进行综合分析的先验知识。我们引入了一种基于网络的基于RNA测序的转录本定量（Net-RSTQ）方法，将蛋白质结构域-结构域相互作用网络与短读段比对相结合，用于转录本丰度估计。基于我们的观察，即网络中结构域-结构域相互作用的相邻同种型的丰度是正相关的，Net-RSTQ将相邻转录本的表达建模为Dirichlet先验，其基于观察到的针对一个基因中的转录本的读段比对的可能性。所有基因的转录丰度，然后联合估计与交替优化多个EM问题。在模拟中，当同种型共表达与它们的相互作用相关时，Net-RSTQ有效地改善了同种型转录本的定量。对干细胞系、卵巢癌细胞系和乳腺癌细胞系中25个多亚型基因的qRT-PCR结果也表明，Net-RSTQ估计的亚型比例与RNA-Seq数据更一致。在对癌症基因组图谱（TCGA）中的RNA-Seq数据的实验中，通过Net-RSTQ估计的转录本丰度对于卵巢癌、乳腺癌和肺癌的患者样本分类更有信息量。所有实验结果共同支持Net-RSTQ是一种有前途的异构体定量方法。Net-RSTQ工具箱可在http://compbio.cs.umn.edu/Net-RSTQ/上获得。新的测序技术的转录组范围内的RNA谱极大地促进了在异构体为基础的功能表征的细胞系统的兴趣。阐明基因表达的异构体分辨率可能会导致新的分子机制，如基因调控和替代剪接，并可能更好的分子信号表型预测。然而，仅仅基于短读段比对来推导基因的同种型的比例可能过于乐观。从本质上讲，RNA文库制备的系统性采样偏差和重叠亚型中读取起源的模糊性在可靠性方面造成了问题。本文的工作是检验使用蛋白质结构域-结构域相互作用作为异构体转录本定量的先验知识的可能性。我们首先观察到蛋白质结构域-结构域相互作用与TCGA数据中的同种型共表达正相关，然后设计了概率EM方法来整合结构域-结构域相互作用与短读段比对以估计同种型比例。通过对三种细胞系的qRT-PCR实验、几种癌症类型的TCGA患者样本的模拟和分类验证，Net-RSTQ被证明是功能基因组和系统生物学中基于异构体的分析的有用工具。
High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/. New sequencing technologies for transcriptome-wide profiling of RNAs have greatly promoted the interest in isoform-based functional characterizations of a cellular system. Elucidation of gene expressions at the isoform resolution could lead to new molecular mechanisms such as gene-regulations and alternative splicings, and potentially better molecular signals for phenotype predictions. However, it could be overly optimistic to derive the proportion of the isoforms of a gene solely based on short read alignments. Inherently, systematical sampling biases from RNA library preparation and ambiguity of read origins in overlapping isoforms pose a problem in reliability. The work in this paper exams the possibility of using protein domain-domain interactions as prior knowledge in isoform transcript quantification. We first made the observation that protein domain-domain interactions positively correlate with isoform co-expressions in TCGA data and then designed a probabilistic EM approach to integrate domain-domain interactions with short read alignments for estimation of isoform proportions. Validated by qRT-PCR experiments on three cell lines, simulations and classifications of TCGA patient samples in several cancer types, Net-RSTQ is proven a useful tool for isoform-based analysis in functional genomes and systems biology.